Phosphatidylethanol as Blood Biomarker of Alcohol Consumption in Early Pregnancy: An Observational Study in 4,067 Pregnant Women.

BACKGROUND: The teratogenic effects of alcohol are well documented, but there is a lack of screening methods to detect alcohol use during pregnancy. Phosphatidylethanol 16:0/18:1 (PEth) is a specific and sensitive biomarker reflecting alcohol intake up to several weeks after consumption. The aim of this study was to investigate the prevalence of positive PEth values as an indicator of early prenatal alcohol exposure in a general population of pregnant women. METHODS: Rhesus typing is routinely performed in Norway in all pregnancies around gestational week 12. Rhesus-negative women have an additional test taken around week 24. Blood samples submitted to St. Olav University Hospital in Trøndelag, Norway, for Rhesus typing during the period September 2017 to October 2018 were collected. A total of 4,533 whole blood samples from 4,067 women were analyzed for PEth (limit of quantification of 0.003 µM). RESULTS: Fifty-eight women had a positive PEth sample. Of these, 50 women were positive around gestational week 12, 3 women were positive around week 24, and in 5 cases, the timing was unknown. There were no significant differences in proportions of women with positive PEth values related to age, or rural versus urban residency. CONCLUSION: In an unselected pregnant population in Norway, 1.4% had a positive PEth sample around gestational week 12, whereas 0.4% had a positive sample around week 24. The use of PEth as an alcohol biomarker should be further investigated as a diagnostic tool in the antenatal setting.

External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis.

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. CONCLUSIONS: The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. TRIAL REGISTRATION: PROSPERO ID: CRD42015029349 .

Distinct First Trimester Cytokine Profiles for Gestational Hypertension and Preeclampsia.

OBJECTIVE: Gestational hypertension and preeclampsia involve dysregulated maternal inflammatory responses to pregnancy, but whether such responses differ between the disorders has not been determined. We aimed to investigate disease-specific early pregnancy serum cytokine profiles of women subsequently developing gestational hypertension or preeclampsia for new insight into the underlying pathogeneses and differences between the disorders. APPROACH AND RESULTS: The study cohort consisted of 548 pregnant Norwegian women who were either multiparous with previous gestational hypertension or preeclampsia or were nulliparous. Maternal sera at gestational weeks 11(0)-13(6) were assayed for 27 cytokines, C-reactive protein, total cholesterol, high-density lipoprotein, triglyceride, creatinine, calcium, uric acid, and placental growth factor. Compared with normotensive women, women with both hypertensive conditions presented an atherogenic lipid profile at early gestation, but only those later developing gestational hypertension had significantly higher serum levels of interleukin (IL)-5 and IL-12. Comparing the 2 hypertensive pregnancy disorders, women subsequently developing gestational hypertension had higher serum levels of IL-1ß, IL-5, IL-7, IL-8, IL-13, basic fibroblast growth factor, and vascular endothelial growth factor than the women subsequently developing preeclampsia. CONCLUSIONS: This study identifies early pregnancy differences in serum cytokine profiles for gestational hypertension and preeclampsia.

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study.

Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

A prospective study of screening for hypertensive disorders of pregnancy at 11-13 weeks in a Scandinavian population.

OBJECTIVE: To investigate the prediction of preeclampsia and gestational hypertension using maternal characteristics, mean arterial pressure (MAP), uterine artery pulsatility index (UtAPI), pregnancy-associated plasma protein-A (PAPP-A) and placental growth factor (PlGF) at gestational weeks 11-13 in a Scandinavian population with a medium to high prior risk for developing hypertensive disorders of pregnancy. DESIGN: Prospective screening study. SETTING: National Center for Fetal Medicine, Trondheim, Norway. POPULATION: 579 women who were nulliparous or had a previous history of preeclampsia or gestational hypertension. METHODS: Women were examined between 11(+0) and 13(+6) weeks, with interviews for maternal characteristics and measurements of MAP, UtAPI, PAPP-A and PlGF. The tests were evaluated separately and in combined models with receiver operating characteristics (ROC) curves. MAIN OUTCOME MEASURES: Prediction of preeclampsia, severe preeclampsia and gestational hypertension. RESULTS: The best model for severe preeclampsia (MAP+UtAPI+PlGF+PAPP-A) achieved an area under the ROC curve of 0.866 [95% confidence interval (95% CI) 0.756-0.976]. The best models for preeclampsia (MAP+UtAPI+age) achieved 0.738 (0.634-0.841), gestational hypertension (MAP) 0.820 (0.727-0.913) and hypertensive disorders in pregnancy overall (MAP+PlGF+age) 0.783 (0.709-0.856). Using the best model we could identify 61.5% (95% CI 31.6-86.1) of severe preeclampsia, 38.5% (95% CI 20.2-59.4) of preeclampsia and 42.9% (95% CI 21.8-66) of gestational hypertension at a fixed 10% false-positive rate. CONCLUSIONS: Maternal characteristics, MAP, UtAPI, PAPP-A and PlGF showed limited value as screening tests. Further research on biochemical and biophysical tests and algorithms combining these parameters is needed before first trimester screening for hypertensive disorders of pregnancy is included in antenatal care in Scandinavia.

A randomized controlled trial of third-trimester routine ultrasound in a non-selected population.

OBJECTIVE: To compare detection rates of small-for-gestational-age fetuses, large-for-gestational-age fetuses, congenital anomalies and adverse perinatal outcomes in pregnancies randomized to third-trimester routine ultrasound or ultrasound on clinical indication. DESIGN: Randomized controlled trial. SETTING: National Center for Fetal Medicine in Norway between 1989 and 1992. POPULATION: A total of 6780 pregnancies from a non-selected population. METHODS: Two routine ultrasound examinations at 18 and 33 weeks were compared with routine ultrasound at 18 weeks and ultrasound on clinical indication. Suspected small-for-gestational-age fetuses were followed with serial scans and cardiotocography. Doppler ultrasound was not used. MAIN OUTCOME MEASURES: Detection rates of small-for-gestational-age and large-for-gestational-age fetuses, congenital anomalies and adverse perinatal outcomes. RESULTS: Third trimester routine ultrasound improved detection rates of small-for-gestational-age fetuses from 46 to 80%, but overall perinatal morbidity and mortality remained unchanged. Detection of large-for-gestational-age fetuses increased from 36 to 91%. There was a significant increase of induction of labor and elective cesarean sections due to suspected small-for-gestational-age and a significant decrease of induction of labor and elective cesarean sections due to suspected large-for-gestational-age in the study group; there were no other differences regarding intervention. The detection rate of congenital anomalies was 56%, with no significant difference between the groups. CONCLUSIONS: Routine use of third-trimester routine ultrasound increased detection rates of small-for-gestational-age and large-for-gestational-age fetuses. This did not alter perinatal outcomes. Third-trimester ultrasound screening should not be rejected before a policy of adding Doppler surveillance to the high-risk group identified has been investigated further.

Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis.

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. OBJECTIVES: To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. DESIGN: This was an individual participant data meta-analysis of cohort studies. SETTING: Source data from secondary and tertiary care. PREDICTORS: We identified predictors from systematic reviews, and prioritised for importance in an international survey. PRIMARY OUTCOMES: Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia. ANALYSIS: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and τ2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. RESULTS: The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. LIMITATIONS: Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. CONCLUSION: For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. FUTURE WORK: Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate. STUDY REGISTRATION: This study is registered as PROSPERO CRD42015029349. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

WHAT IS THE PROBLEM?: Pre-eclampsia, a condition in pregnancy that results in raised blood pressure and protein in the urine, is a major cause of complications for the mother and baby. WHAT IS NEEDED?: A way of accurately identifying women at high risk of pre-eclampsia to allow clinicians to start preventative interventions such as administering aspirin or frequently monitoring women during pregnancy. WHERE ARE THE RESEARCH GAPS?: Although over 100 tools (models) have been reported worldwide to predict pre-eclampsia, to date their performance in women managed in the UK NHS is unknown. WHAT DID WE PLAN TO DO?: We planned to comprehensively identify all published models that predict the risk of pre-eclampsia occurring at any time during pregnancy and to assess if this prediction is accurate in the UK population. If the existing models did not perform satisfactorily, we aimed to develop new prediction models. WHAT DID WE FIND?: We formed the International Prediction of Pregnancy Complications network, which provided data from a large number of studies (78 studies, 25 countries, 125 researchers, 3,570,993 singleton pregnancies). We were able to assess the performance of 24 out of the 131 models published to predict pre-eclampsia in 11 UK data sets. The models did not accurately predict the risk of pre-eclampsia across all UK data sets, and their performance varied within individual data sets. We developed new prediction models that showed promising performance on average across all data sets, but their ability to correctly identify women who develop pre-eclampsia varied between populations. The models were more clinically useful when used in the care of first-time mothers pregnant with one child, compared to a strategy of treating them all as if they were at high-risk of pre-eclampsia. WHAT DOES THIS MEAN?: Before using the International Prediction of Pregnancy Complications models in various populations, they need to be adjusted for characteristics of the particular population and the setting of application.

Placental inflammation by HMGB1 activation of TLR4 at the syncytium.

INTRODUCTION: Normal pregnancy is characterized by an elevated inflammatory state involving the placenta. The placental inflammation is further increased in preeclampsia, resulting in release of harmful danger signals to the maternal circulation. Activation of toll-like receptors (TLR)2 and TLR4 by endogenous danger signals plays a role in inflammatory diseases. Placental TLR2 and TLR4 expression has been reported, and high mobility group box 1 (HMGB1) is a likely endogenous activator of these receptors. We aimed to examine HMGB1 activation of TLR2 and TLR4 as mechanisms of placental inflammation in normal and preeclamptic pregnancies, by combined analysis of expression and function of the ligand HMGB1, the receptors TLR2 and TLR4, and the cytokine responder interleukin (IL)-8. METHODS: Protein expression was analyzed in placental tissue from normal and preeclamptic pregnancies, and cytokine responses to two distinct HMGB1 isoforms were examined in placental explants and trophoblasts. Inflammatory and anti-angiogenic markers were measured in maternal serum. RESULTS: We demonstrated strong co-localized expression of HMGB1, TLR4 and IL-8 in the syncytium layer of the placenta. Syncytium TLR4 expression and maternal serum levels of IL-8 were significantly increased in preeclamptic compared to normal pregnancies. Functionality was confirmed by TLR4-dependent release of IL-8 from placental explants and trophoblasts in response to the inflammatory isoform of HMGB1. DISCUSSION: This demonstrates a role for the HMGB1-TLR4 pathway at the syncytium layer and suggests involvement in placental inflammation and preeclampsia.